Course Overview
This course explores how to use Databricks and Apache Spark on Azure to take data projects from exploration to production. You’ll learn how to ingest, transform, and analyze large-scale datasets with Spark DataFrames, Spark SQL, and PySpark, while also building confidence in managing distributed data processing. Along the way, you’ll get hands-on with the Databricks workspace—navigating clusters and creating and optimizing Delta tables. You’ll also dive into data engineering practices, including designing ETL pipelines, handling schema evolution, and enforcing data quality. The course then moves into orchestration, showing you how to automate and manage workloads with Lakeflow Jobs and pipelines. To round things out, you’ll explore governance and security capabilities such as Unity Catalog and Purview integration, ensuring you can work with data in a secure, well-managed, and production-ready environment.
Moyens d'évaluation :
- Quiz pré-formation de vérification des connaissances (si applicable)
- Évaluations formatives pendant la formation, à travers les travaux pratiques réalisés sur les labs à l’issue de chaque module, QCM, mises en situation…
- Complétion par chaque participant d’un questionnaire et/ou questionnaire de positionnement en amont et à l’issue de la formation pour validation de l’acquisition des compétences
Who should attend
Before taking this course, learners should already be comfortable with the fundamentals of Python and SQL. This includes being able to write simple Python scripts and work with common data structures, as well as writing SQL queries to filter, join, and aggregate data. A basic understanding of common file formats such as CSV, JSON, or Parquet will also help when working with datasets. In addition, familiarity with the Azure portal and core services like Azure Storage is important, along with a general awareness of data concepts such as batch versus streaming processing and structured versus unstructured data. While not mandatory, prior exposure to big data frameworks like Spark, and experience working with Jupyter notebooks, can make the transition to Databricks smoother.
Prerequisites
Before starting this learning path, you should already be comfortable with the fundamentals of Python and SQL. This includes being able to write simple Python scripts and work with common data structures, as well as writing SQL queries to filter, join, and aggregate data. A basic understanding of common file formats such as CSV, JSON, or Parquet will also help when working with datasets.
In addition, familiarity with the Azure portal and core services like Azure Storage is important, along with a general awareness of data concepts such as batch versus streaming processing and structured versus unstructured data. While not mandatory, prior exposure to big data frameworks like Spark, and experience working with Jupyter notebooks, can make the transition to Databricks smoother.
Course Content
Explore Azure Databricks
- Introduction
- Get started with Azure Databricks
- Identify Azure Databricks workloads
- Understand key concepts
- Data governance using Unity Catalog and Microsoft Purview
- Exercise - Explore Azure Databricks
- Module assessment
- Summary
Perform data analysis with Azure Databricks
- Introduction
- Ingest data with Azure Databricks
- Data exploration tools in Azure Databricks
- Data analysis using DataFrame APIs
- Exercise - Explore data with Azure Databricks
- Module assessment
- Summary
Use Apache Spark in Azure Databricks
- Introduction
- Get to know Spark
- Create a Spark cluster
- Use Spark in notebooks
- Use Spark to work with data files
- Visualize data
- Exercise - Use Spark in Azure Databricks
- Module assessment
- Summary
Manage data with Delta Lake
- Introduction
- Get started with Delta Lake
- Create Delta tables
- Implement schema enforcement
- Data versioning and time travel in Delta Lake
- Data integrity with Delta Lake
- Exercise - Use Delta Lake in Azure Databricks
- Module assessment
- Summary
Build Lakeflow Declarative Pipelines
- Introduction
- Explore Lakeflow Declarative Pipelines
- Data ingestion and integration
- Real-time processing
- Exercise - Create a Lakeflow Declarative Pipeline
- Module assessment
- Summary
Deploy workloads with Lakeflow Jobs
- Introduction
- What are Lakeflow Jobs?
- Understand key components of Lakeflow Jobs
- Explore the benefits of Lakeflow Jobs
- Deploy workloads using Lakeflow Jobs
- Exercise - Create a Lakeflow Job
- Module assessment
- Summary
Anglais
Allemand
Polonais
ukrainien
Pays-Bas
Pologne
Ukraine
Arabie Saoudite
Emirats-Arabes-Unis
Moyens Pédagogiques :